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Related Experiment Video

Updated: Oct 30, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

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Graph Representation Integrating Signals for Emotion Recognition and Analysis.

Teresa Zawadzka¹, Tomasz Wierciński¹, Grzegorz Meller¹

¹Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework. GRISERA enables consistent integration and retrieval of multimodal data for Affective Computing research, enhancing data reusability.

Keywords:

affective computing biosignals datasets emotion recognition graph databases signal integration

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Area of Science:

Affective Computing
Data Science
Signal Processing

Background:

Data reusability is crucial in scientific research.
Affective Computing relies on diverse datasets (biosignals, video, images).
Creating large datasets for Affective Computing is costly and time-consuming.

Purpose of the Study:

To present a novel framework for integrating and storing multimodal experimental data.
To address the challenge of data reusability in Affective Computing.
To enable efficient data retrieval for analysis and model training.

Main Methods:

Development of the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework.
Utilizing a standardized graph model for data representation and storage.
Implementing methods for data integration and query pattern creation.

Main Results:

Demonstrated successful storage and retrieval of data from the AMIGOS dataset for deep learning.
Validated the integration of signals from multiple sources (AMIGOS, ASCERTAIN, DEAP) within GRISERA.
Showcased the framework's capability for subsequent statistical analysis.

Conclusions:

GRISERA offers a persistent and consistent model for integrated signals in Affective Computing.
The framework facilitates data reusability across different experiments and datasets.
GRISERA is the first approach to address multi-experiment data integration and retrieval in this field.